LLM coding agents such as Claude Code and OpenAI Codex can drive penetration-testing tools, but they reason about an engagement as a flat list of findings in a context window. Attackers think in graphs: isolated low-severity exposures turn catastrophic once chained through credential capture, privilege escalation, and lateral movement. This gap feeds a common belief that AI agents cannot truly chain exploitation; we argue this describes the flat context window, not the model. We present ChainGraph, a pattern that externalizes exploit-chain reasoning into a Neo4j property graph wired to an off-the-shelf agent via the Model Context Protocol, no bespoke harness. Findings, attack steps, credentials, assets, and tamper-evident evidence receipts are typed nodes with pre/post-condition edges; JSON/Markdown holds documentary memory while Neo4j holds reasoning memory. We formalize five operator questions (next-step inference, entry-to-crown-jewel pathing, combined-severity promotion, evidence provenance, and chokepoint identification) as reusable Cypher and Graph Data Science queries, plus a chain-severity score distinct from per-finding local severity. On a synthetic multi-forest Active Directory engagement modeled on GOAD, in live Neo4j with GDS, three findings rated Medium in isolation become Critical once their path to the forest root is materialized, and betweenness centrality names the best chain break-points. Scoping each decision to a graph result rather than re-reading the engagement cut the reasoning payload by about 98% on a token proxy. We give a reproducible setup for Claude Code and Codex. Results are illustrative single-run measurements, not a benchmark.

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